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» Explaining inferences in Bayesian networks
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ILP
2005
Springer
14 years 1 months ago
Deriving a Stationary Dynamic Bayesian Network from a Logic Program with Recursive Loops
Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never s...
Yi-Dong Shen, Qiang Yang
BMCBI
2007
114views more  BMCBI 2007»
13 years 7 months ago
Large scale statistical inference of signaling pathways from RNAi and microarray data
Background: The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray tec...
Holger Fröhlich, Mark Fellmann, Holger Sü...
ASC
2000
13 years 9 months ago
A New Object-Oriented Stochastic Modeling Language
A new language and inference algorithm for stochastic modeling is presented. This work refines and generalizes the stochastic functional language originally proposed by [1]. The l...
Daniel Pless, George F. Luger, Carl R. Stern
NIPS
2000
13 years 9 months ago
Learning Switching Linear Models of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data usi...
Vladimir Pavlovic, James M. Rehg, John MacCormick
MMAS
2011
Springer
13 years 2 months ago
Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems
While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and thei...
Phaedon-Stelios Koutsourelakis, Elias Bilionis